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HalalStocksFilter.py
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HalalStocksFilter.py
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'''
Apache License
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'''
import pandas as pd
import datetime
from bs4 import BeautifulSoup
from urllib import request
import json
import re
from tqdm import tqdm
import os
import argparse
import sys
class HalalStocksFilter:
def __init__(self):
self.desc = "Class is HalalScreening for retrieval of data from various websites per stock ticker/symbol in" \
" order to perform screening."
def get_screen_data(self, stocks, unique=False, compression=None, verbose=0, user_agent=''):
# Create a dataframe with necessary columns
df = pd.DataFrame(columns=list(['Ticker', # 0
'Company Name', # 1
'Company Description', # 2
'Trailing 24-Month Average Market Capitalization (MkCap)', # 3
'Total Debt (TD)', # 4
'Cash & Short Term Investments (Cash)', # 5
'Non-Operating Interest Income (Sec)', # 6
'Sum of Cash & Interest-Bearing Securities (Cash+Sec)', # 7
'Accounts Receivables (AR)', # 8
'TD/MkCap Ratio', # 9
'Cash+Sec/MkCap Ratio', # 10
'AR/MkCap Ratio', # 11
'Fiscal Data Year (TD)', # 12
'Fiscal Data Year (Sec)', # 13
'Fiscal Data Year (Cash)', # 14
'Fiscal Data Year (AR)', # 15
'Description Screening', # 16
'TD/MkCap < 33%', # 17
'CashSec/MkCap < 33%', # 18
'AR/MkCap Ratio < 33%', # 19
'Final Result'])) # 20
compname_tag=0
# Check if input is a list of stock symbols or is a dataframe with a column of stock symbols
if isinstance(stocks,list):
df['Ticker'] = stocks
elif isinstance(stocks,pd.DataFrame):
for i in ['Ticker', 'Symbol', 'Stock Symbol', 'Ticker Symbol', 'Stock Ticker']:
for j in stocks.columns:
if (i in j) == True:
break
if (i in j) == True:
break
df['Ticker'] = stocks[j]
for i in ['Company', 'Company Name', 'Name']:
for j in stocks.columns:
if (i in j) == True:
break
if (i in j) == True:
df['Company Name'] = stocks[j]
compname_tag=1
break
elif isinstance(stocks,str):
try:
stocks = pd.read_csv(stocks)
except:
try:
stocks = pd.read_csv(stocks, sep=',', header=0, index_col=0, encoding='windows-1252')
except:
try:
stocks = pd.read_csv(stocks, sep=',', header=0, index_col=0, encoding='utf-16')
except:
try:
stocks = pd.read_csv(stocks, sep=',', header=0, index_col=0, encoding='ISO-8859-1')
except:
try:
stocks = pd.read_excel(stocks)
except:
try:
stocks = pd.read_json(stocks)
except:
try:
stocks = pd.read_pickle(stocks)
except:
try:
stocks = pd.read_sas(stocks)
except:
try:
stocks = pd.read_sql(stocks)
except:
try:
stocks = pd.read_sql_query(stocks)
except:
try:
stocks = pd.read_sql_table(stocks)
except:
try:
stocks = pd.read_stata(stocks)
except:
try:
stocks = pd.read_parquet(stocks)
except:
try:
stocks = pd.read_table(stocks)
except:
try:
stocks = pd.read_html(stocks)
except:
raise Exception('Input is neither a list of '
'stock symbols nor a (path to/'
'actual pandas) dataframe with a'
' stocks symbols column. Please '
're-check in put.')
for i in ['Ticker', 'Symbol', 'Stock Symbol', 'Ticker Symbol', 'Stock Ticker']:
for j in stocks.columns:
if (i in j) == True:
break
if (i in j) == True:
break
df['Ticker'] = stocks[j]
for i in ['Company', 'Company Name', 'Name']:
for j in stocks.columns:
if (i in j) == True:
break
if (i in j) == True:
df['Company Name'] = stocks[j]
break
else:
raise Exception('Input is neither a list of stock symbols nor a (pandas) dataframe with stocks symbols'
' column. Please re-check input.')
numrows = df.shape[0]
# Make some folders if nonexistent
# if not os.path.exists('.\json'):
# os.makedirs('.\json')
# temp_path = '.\json'
count = 0
if user_agent == '':
user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0'
for count in tqdm(range(numrows)):
if verbose >=1:
print(str(df.iloc[count, 0]) + ": started.")
###########################################
#### Market Cap (Zacks website) ####
###########################################
if verbose >= 2:
print("Acquiring Market Cap for stock number " + str(count+1) + ": "
+ str(df.iloc[count, 0]) + " from Zacks.")
tries=0
while tries<5:
try:
url = "https://widget3.zacks.com/data/chart/json/" + str(
df.iloc[count, 0]) + "/market_cap/www.zacks.com"
req = request.Request(url, data=None, headers={'User-Agent':user_agent})
r = request.urlopen(req)
market_cap = json.load(r)
monthly_market_cap = market_cap["monthly_market_cap"]
timestamps = list(monthly_market_cap.keys())
dates = [datetime.datetime.strptime(ts, "%m/%d/%y") for ts in timestamps]
dates.sort(reverse=True)
sorteddates = [datetime.datetime.strftime(ts, "%m/%d/%y") for ts in dates]
# calculate the 24-month trailing average market cap of company
i = 0
j = 24
mrktcp24 = 0
while i < 24:
try:
mrktcp24 += float(monthly_market_cap[sorteddates[i]])
except:
mrktcp24 = mrktcp24
j -= 1
i += 1
mrktcp24avg = mrktcp24 / j
df.iloc[count, 3] = mrktcp24avg
break
except:
tries+=1
if tries==5:
if verbose >= 3:
print("Failed to get Market Cap for stock number " + str(count) + ": "
+ str(df.iloc[count, 1]))
print("Setting Market Cap for stock number " + str(count)
+ " to 0.0000001")
df.iloc[count, 3] = 0.0000001
if verbose >= 2:
print("Dataframe: " + str(df.iloc[count, 0]) + " data retrieval 33% complete.")
###########################################################################################################
#### Company Name, Company Description, Cash and Short Term Investments, ####
#### Non-Operating Interest Income, Accounts Receivable, Total Debt (WSJ and back-up websites) ####
###########################################################################################################
if verbose >= 3:
print("Acquiring the Company Name and Description for stock number " + str(count+1) + ": "
+ str(df.iloc[count, 0]) + " from the WSJ.")
elif verbose >= 2:
print("Acquiring the Company Name and Description for stock number " + str(count + 1) + ": "
+ str(df.iloc[count, 0]))
### Get the company 'Description' and the 'Company Name'
url = ("http://quotes.wsj.com/" + str(df.iloc[count, 0]) + "/company-people")
tries=0
while tries<5:
tries+=1
## Get company 'Description'
try:
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
# Get Description
results = soup.find("div", {"class": "cr_description_full cr_expand"}).find("p",{"class": "txtBody"})
df.iloc[count, 2] = results.text
except:
df.iloc[count, 2] = "N/A"
## If company 'Description' not found on WSJ, try Zacks
url = ("https://www.zacks.com/stock/quote/" + str(df.iloc[count, 0]))
if df.iloc[count, 2] == "N/A":
try:
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
# Get Description
results = soup.find("section", {"id": "stock_comp_desc"}).find("p")
df.iloc[count, 2] = results.text
except:
df.iloc[count, 2] = "N/A"
## Get the 'Company Name'
if compname_tag==0:
try:
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
# Get Name
results = soup.find("div", {"class": "quote_summary"}).find("h1")
df.iloc[count, 1] = results.text.split("(")[0]
except:
df.iloc[count, 1] = "N/A"
break
if verbose >= 3:
print("Acquiring the Total Debt for stock number " + str(count+1) + ": "
+ str(df.iloc[count, 0]) + " from the WSJ.")
elif verbose >= 2:
print("Acquiring the Total Debt for stock number " + str(count + 1) + ": "
+ str(df.iloc[count, 0]))
### Get the 'Total Debt'
tries=0
url = "http://quotes.wsj.com/" + str(df.iloc[count, 0]) + "/financials"
while tries < 5:
tries+=1
try:
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
break
except:
if verbose >= 3:
print("Attempt " + str(tries + 1) + " at retrieving the 'Total Debt' from WSJ")
try:
check = soup.find("section", {"class": "sector cr_section_2"}).find("span", {"class": "data_none"}).text
except:
check = ''
FiscalDataYear3 = '0000'
tries=0
if check != 'Data not available.':
while tries < 5:
try:
results = soup.find("div", {"data-module-id": "8"}).find("table", {
"class": "cr_financials_table"}).find("tbody").find_next_sibling().find("tr", {
"class": "cr_financials_row"}).find_all("th")
FiscalDataYear3 = results[1].text
results = soup.find("div", {"data-module-id": "8"}).find("table", {
"class": "cr_financials_table"}).find("tbody").find_next_sibling().find("tr", {
"class": "cr_financials_row"}).find_next_siblings()
try:
match1 = re.search(r'\sBi*', results[1].text).group()[1]
except AttributeError:
match1 = False
try:
match2 = re.search(r'\sMi*', results[1].text).group()[1]
except AttributeError:
match2 = False
if match1:
value = 1000.0
elif match2:
value = 1.0
else:
value = 1.0 / 1000
try:
df.iloc[count, 4] = float(re.search(r'\d+\.*\d*', results[1].text).group()) * value
except:
df.iloc[count, 4] = 0
break
except:
tries+=1
if tries==5:
df.iloc[count, 4] = 'N/A'
else:
df.iloc[count, 4] = "N/A"
# If Total Debt not found on WSJ, try Zacks
if df.iloc[count, 4] == 'N/A':
if verbose >= 3:
print("Total Debt not found on WSJ. Attempting to retrieve sum of ['Current Portion Long-Term Debt' "
"+ 'Long-Term Debt' + 'Convertible Debt'] from Zacks.")
tries=0
while tries<5:
try:
url = "https://www.zacks.com/stock/quote/" + str(df.iloc[count, 0]) + "/balance-sheet"
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
results = soup.find("div", {"id": "annual_income_statement"}).find("table").find("thead").find("tr").find_all("th")[1].text
FiscalDataYear3 = results.split('/')[2]
results = soup.find("div", {"id": "annual_income_statement"}).find_all("p")[1].text
try:
match1 = re.search(r'\sBi*', results).group()[1]
except AttributeError:
match1 = False
try:
match2 = re.search(r'\sMi*', results).group()[1]
except AttributeError:
match2 = False
if match1:
value = 1000.0
elif match2:
value = 1.0
else:
value = 1.0 / 1000
results = soup.find("div", {"id": "annual_income_statement"}).find("table").find_next("table").find(
"tbody").find_all("tr")
count2 = 0
totalDebt = 0
while count2 < len(results):
name = results[count2].find("td").text
if name == 'Current Portion Long-Term Debt':
totalDebt += float(results[count2].find_all("td")[1].text.replace(",", ""))
elif name == 'Long-Term Debt':
totalDebt += float(results[count2].find_all("td")[1].text.replace(",", ""))
elif name == 'Convertible Debt':
totalDebt += float(results[count2].find_all("td")[1].text.replace(",", ""))
count2 += 1
try:
df.iloc[count, 4] = totalDebt * value
except:
df.iloc[count, 4] = 0
break
except:
tries+=1
### Get the latest Fiscal Year, 'Cash and Short Term Investments', and 'Total Accounts Receivable'
if verbose >= 3:
print("Acquiring the Cash and Short Term Investments, and Total Accounts Receivable, for latest Fiscal"
" Year available, for stock number " + str(count + 1) + ": " + str(df.iloc[count, 0]) + " from the WSJ.")
elif verbose >= 2:
print("Acquiring the Cash and Short Term Investments, and Total Accounts Receivable, for latest Fiscal"
" Year available, for stock number " + str(count + 1) + ": " + str(
df.iloc[count, 0]))
url = "http://quotes.wsj.com/" + str(df.iloc[count, 0]) + "/financials/annual/balance-sheet"
tries=0
while tries<5:
try:
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
# Get latest Fiscal Year on website
try:
results = soup.find("table", {"class": "cr_dataTable"}).find("thead").find("tr").find_all("th")
FiscalDataYear1 = results[1].text
except:
FiscalDataYear1 = '0000'
# Get the base of values: in thousands, or in millions
if FiscalDataYear1 != '0000':
BaseValue = results[0].text
try:
match1 = re.search(r'thousands', BaseValue.lower())
except:
match1 = False
try:
match2 = re.search(r'millions', BaseValue.lower())
except:
match2 = False
try:
match3 = re.search(r'billions', BaseValue.lower())
except:
match3 = False
if match1:
value = 1000.0
elif match2:
value = 1.0
elif match3:
value = 1.0 / 1000
break
except:
tries += 1
if tries == 5:
FiscalDataYear1 = '0000'
tries=0
while tries<5:
try:
# Get the rest of the data
results = soup.find("table", {"class": "cr_dataTable"}).find("tbody").find_all("tr")
# Get 'Cash and Short Term Investments'
num = results[2].findChildren("td")[1].text
try:
df.iloc[count, 5] = float(num.replace(",", "")) / value
except ValueError:
df.iloc[count, 5] = 'N/A'
# Get 'Total Accounts Receivable'
try:
num = results[13].findChildren("td")[1].text
try:
df.iloc[count, 8] = float(num.replace(",", "")) / value
except ValueError:
df.iloc[count, 8] = 'N/A'
except:
df.iloc[count, 8] = 'N/A'
break
except:
tries+=1
if tries==5:
df.iloc[count, 5] = 'N/A'
df.iloc[count, 8] = 'N/A'
# If Total Cash and Short Term Investments not found on WSJ, try Zacks
tries = 0
if df.iloc[count, 5] == 'N/A':
if verbose >= 3:
print("Total Cash and Short Term Investments not found on WSJ. Attempting to retrieve 'Cash & Equivalents' from Zacks.")
while tries < 5:
try:
url = "https://www.zacks.com/stock/quote/" + str(df.iloc[count, 0]) + "/balance-sheet"
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
results = soup.find("div", {"id": "annual_income_statement"}).find("table").find("thead").find(
"tr").find_all("th")[1].text
FiscalDataYear1 = results.split('/')[2]
results = soup.find("div", {"id": "annual_income_statement"}).find_all("p")[1].text
try:
match1 = re.search(r'\sBi*', results).group()[1]
except AttributeError:
match1 = False
try:
match2 = re.search(r'\sMi*', results).group()[1]
except AttributeError:
match2 = False
if match1:
value = 1000.0
elif match2:
value = 1.0
else:
value = 1.0 / 1000
results = soup.find("div", {"id": "annual_income_statement"}).find("table").find("tbody").find_all("tr")
count2 = 0
while count2 < len(results):
name = results[count2].find("td").text
if name == 'Cash & Equivalents':
cash = results[count2].find_all("td")[1].text.replace(",","")
count2 += 1
try:
df.iloc[count, 5] = float(cash) * value
except:
df.iloc[count, 5] = 'N/A'
break
except:
tries = +1
if tries==5:
FiscalDataYear1 = '0000'
df.iloc[count, 5] = 'N/A'
# If Total Accounts Receivable not found on WSJ, try Zacks
tries=0
if df.iloc[count, 8] == 'N/A':
if verbose >= 3:
print("Total Accounts Receivable not found on WSJ. Attempting to retrieve 'Receivables' from Zacks.")
while tries<5:
try:
url = "https://www.zacks.com/stock/quote/" + str(df.iloc[count, 0]) + "/balance-sheet"
try:
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
results = soup.find("div", {"id": "annual_income_statement"}).find("table").find("thead").find(
"tr").find_all("th")[1]
except:
continue
FiscalDataYear1 = results.text.split('/')[2]
results = soup.find("div", {"id": "annual_income_statement"}).find_all("p")[1].text
try:
match1 = re.search(r'\sBi*', results).group()[1]
except AttributeError:
match1 = False
try:
match2 = re.search(r'\sMi*', results).group()[1]
except AttributeError:
match2 = False
if match1:
value = 1000.0
elif match2:
value = 1.0
else:
value = 1.0 / 1000
results = soup.find("div", {"id": "annual_income_statement"}).find("table").find("tbody").find_all(
"tr")
count2 = 0
while count2 < len(results):
name = results[count2].find("td").text
if name == 'Receivables':
acntRec = results[count2].find_all("td")[1].text.replace(",", "")
break
count2 += 1
try:
df.iloc[count, 8] = float(acntRec) * value
except:
df.iloc[count, 8] = 'N/A'
tries=5
except:
tries += 1
df.iloc[count, 8] = 'N/A'
if verbose >= 2:
print("Dataframe: " + str(df.iloc[count, 0]) + " data retrieval 66% complete.")
### Get the 'Non-Operating Interest Income'
if verbose >= 3:
print("Acquiring the Non-Operating Interest Income for stock number " + str(count + 1) + ": " +
str(df.iloc[count, 0]) + " from the WSJ.")
elif verbose >= 2:
print("Acquiring the Non-Operating Interest Income for stock number " + str(count + 1) + ": " +
str(df.iloc[count, 0]))
tries = 0
url = "http://quotes.wsj.com/" + str(df.iloc[count, 0]) + "/financials/annual/income-statement"
while tries < 5:
tries+=1
try:
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req, timeout=60)
soup = BeautifulSoup(r, "html.parser")
break
except:
print("Attempt " + str(tries + 1) + " at retrieving the 'Non-Operating Interest Income' from WSJ")
try:
check = soup.find("section", {"class": "sector cr_section_2"}).find("span", {"class": "data_none"}).text
except:
check = ''
FiscalDataYear2 ='0000'
tries = 0
if check != 'Data not available.':
while tries < 5:
try:
results = soup.find("table", {"class": "cr_dataTable"}).find("thead").find("tr").find_all("th")
FiscalDataYear2 = results[1].text
BaseValue = results[0].text
try:
match1 = re.search(r'thousands', BaseValue.lower())
except:
match1 = False
try:
match2 = re.search(r'millions', BaseValue.lower())
except:
match2 = False
try:
match3 = re.search(r'billions', BaseValue.lower())
except:
match3 = False
if match1:
value = 1000.0
elif match2:
value = 1.0
elif match3:
value = 1.0 / 1000
results = soup.find("table", {"class": "cr_dataTable"}).find("tbody").find_all("tr")
k = 40
try:
while k < 60:
if results[k].findChildren("td")[0].text == 'Non-Operating Interest Income':
num = results[k].findChildren("td")[1].text
break
else:
k += 1
except:
num = "N/A"
try:
df.iloc[count, 6] = (float(num.replace(",", "")) / value)
except:
df.iloc[count, 6] = 0
break
except:
tries+=1
if tries == 5:
FiscalDataYear2 = '0000'
df.iloc[count, 6] = "N/A"
else:
df.iloc[count, 6] = "N/A"
# If Non-Operating Interest Income not found on WSJ, try Reuters
if df.iloc[count, 6] == 'N/A':
if verbose >= 3:
print("Non-Operating Interest Income not found on WSJ. Attempting to retrieve 'Interest/Invest Income - Non-Operating' from Reuters.")
tries = 0
while tries < 5:
tries += 1
for ext in [".O", ".N", ".OQ", ".P", ".PK", ".PH", ".MU"]:
try:
url = 'https://www.reuters.com/finance/stocks/income-statement/' + str(df.iloc[count, 0]) + ext + '?stmtType=INC&perType=ANN'
req = request.Request(url, data=None, headers={'User-Agent': user_agent})
r = request.urlopen(req)
soup = BeautifulSoup(r, "html.parser")
results = soup.find("span", {"class": "units"})
if 'millions' in results.text.lower():
value = 1.0
elif 'billions' in results.text.lower():
value = 1000.0
else:
value = 1.0 / 1000
results = soup.find("th", {"class": "data"})
FiscalDataYear2 = results.text.split("\n")[1].replace(" ", "")
results = soup.find_all("tr")
count2 = 1
while count2 < len(results) - 1:
j = results[count2].find_all("td")
count2 += 1
if (j[0].text == 'Interest/Invest Income - Non-Operating') | (j[0].text == 'Interest/Invest Income - Non-Operating'):
try:
interest = float(j[1].text.replace(",", ""))
except:
try:
interest = -float(j[1].text.replace("(", "").replace(")", "").replace(",", ""))
except:
interest = 0
break
df.iloc[count, 6] = interest * value
break
except:
continue
if df.iloc[count, 6] != "N/A":
break
df.iloc[count, 12] = FiscalDataYear3
df.iloc[count, 13] = FiscalDataYear2
df.iloc[count, 14] = FiscalDataYear1
df.iloc[count, 15] = FiscalDataYear1
if verbose >= 1:
print("Dataframe: Stock #" + str(count) + " (" + str(df.iloc[count, 0]) + ") data retrieval 100% complete.")
##########################################
### Saving the dataframe to a csv file ###
##########################################
now = datetime.datetime.now()
if unique==False:
dateversion = now.strftime("%Y-%m-%d")
elif (unique.lower()=="no") | (unique.lower()=="false"):
dateversion = now.strftime("%Y-%m-%d")
else:
dateversion = now.strftime("%Y-%m-%d-%H.%M.%S")
if not os.path.exists(str(dateversion)):
os.mkdir(str(dateversion))
filename = str(dateversion)+'\Full_ScreeningData_' + str(dateversion) + '.csv'
df.to_csv(path_or_buf=filename, mode='w+', compression=compression)
print("All data retrieval 100% complete.")
return df, filename
def apply_screen(self, df_or_filename, haram_keywords=None, output=0, length=0, unique=False, compression=None, mask=None):
# Check that 'df_or_filename' is a filepath and try to read
if isinstance(df_or_filename, str): #|isinstance(df_or_filename, basestring)
try:
df = pd.read_csv(df_or_filename, header=0, index_col=0)
except:
try:
df = pd.read_csv(df_or_filename, sep=',', header=0, index_col=0, encoding = 'windows-1252')
except:
try:
df = pd.read_csv(df_or_filename, sep=',', header=0, index_col=0, encoding = 'utf-16')
except:
try:
df = pd.read_csv(df_or_filename, sep=',',header=0, index_col=0, encoding='ISO-8859-1')
except:
try:
df = pd.read_excel(df_or_filename)
except:
try:
df = pd.read_json(df_or_filename)
except:
try:
df = pd.read_pickle(df_or_filename)
except:
try:
df = pd.read_sas(df_or_filename)
except:
try:
df = pd.read_sql(df_or_filename)
except:
try:
df = pd.read_sql_query(df_or_filename)
except:
try:
df = pd.read_sql_table(df_or_filename)
except:
try:
df = pd.read_stata(df_or_filename)
except:
try:
df = pd.read_parquet(df_or_filename)
except:
try:
df = pd.read_table(df_or_filename)
except:
try:
df = pd.read_html(df_or_filename)
except:
raise("Error encountered with reading the parameter \
'df_or_filename' as a filepath into a pandas dataframe.")
# Check that 'df_or_filename' is a dataframe and try to read
elif isinstance(df_or_filename, pd.DataFrame):
try:
df = df_or_filename
except:
raise ("Error encountered with reading the parameter 'df_or_filename' as a pandas dataframe.")
# Final check that 'df_or_filename' is an object that can be converted into a pandas dataframe
else:
try:
df = pd.DataFrame(df_or_filename)
except:
raise ("Function parameter 'df_or_filename' is incorrect type. It must be a filepath or a pandas \
dataframe or object readable as a pandas dataframe")
tag = 0
# Check if 'mask' parameter is empty or correct type and use it accordingly, depending on the dataframe loaded.
# If list of numbers, length of list should be 5 or 6, to represent the indices of the loaded dataframe's columns
# which represent the columns below, in their order below:
# If length 5:
# [ Company Description ; Trailing 24-Month Average Market Capitalization (MkCap) ; Total Debt (TD) ;
# Sum of Cash & Interest-Bearing Securities (Cash+Sec) ; Accounts Receivables (AR) ]
# If length 6:
# [ Company Description ; Trailing 24-Month Average Market Capitalization (MkCap) ; Total Debt (TD) ;
# Cash & Short Term Investments (Cash); Non-Operating Interest Income (Sec) ; Accounts Receivables (AR) ]
count = 0
if isinstance(mask,list):
count = len(mask)
if 5 == count:
df_CompDes = df.iloc[:,mask[0]]
df_MkCap = df.iloc[:, mask[1]]
df_TD = df.iloc[:, mask[2]]
df_Cash_Sec = df.iloc[:, mask[3]]
df_AR = df.iloc[:, mask[4]]
tag = 1
if 6 == count:
df_CompDes = df.iloc[:, mask[0]]
df_MkCap = df.iloc[:, mask[1]]
df_TD = df.iloc[:, mask[2]]
df_Cash = df.iloc[:, mask[3]]
df_Sec = df.iloc[:, mask[4]]
df_Cash_Sec = df_Cash + df_Sec
df_AR = df.iloc[:, mask[5]]
tag = 0
# If 'mask' is a dict, make sure keys are written as such, with values representing the indices of the loaded
# dataframe's columns of interest for the screening calculations:
# {'CompDes':#, 'MkCap':#, 'TD':#, 'Cash_Sec':#, 'AR':#}
# or as such:
# {'CompDes':#, 'MkCap':#, 'TD':#, 'Cash':#,'Sec':#, 'AR':#}